Radar systems such as those used for airborne applications typically have to contend with the presence of non-homogeneous clutter, jamming, and dense target clusters. An approach that has proved successful in minimizing the masking effect of undesirable false signals on a target return signal is adaptive matched filtering for signal processing.
Adaptive signal processing systems have many applications including radar reception, cellular telephones, communications systems, and biomedical imaging. Adaptive signal processing systems utilize adaptive filtering to differentiate between the desired signal and the combination of interference and noise, i.e. thermal or receiver noise. An adaptive filter is defined by four aspects: the type of signals being processed, the structure that defines how the output signal of the filter is computed from its input signal, the parameters within this structure that can be iteratively changed to alter the filter's input-output relationship, and the adaptive algorithm that describes how the parameters are adjusted from one time instant to the next.
Common applications of adaptive signal processing include: an adaptive radar reception antenna array, an adaptive antenna array for adaptive communications, and adaptive sonar. In these systems, desired signal detection and estimation is hindered by noise and interference. Interference may be intentional jamming and or unintentional received radiation. Noise is usually described as ever present receiver thermal noise, generally at a low power level. In these applications antenna arrays may change their multidimensional reception patterns automatically in response to the signal environment in a way that optimizes the ratio of signal power to the combination of interference power plus noise power (abbreviated as SINR). The array pattern is easily controlled by weighting the amplitude and phase of the signal from each element before combining (adding) the signals. In general, multidimensional samples may be collected, e.g. over antenna elements, over time, over polarization, etc., where each sample is a separate, input channel to the adaptive processor. Adaptive arrays are especially useful to protect radar and communication systems from interference when the directions of the interference are unknown or changing while attempting to receive a desired signal of known form. Adaptive arrays are capable of operating even when the antenna elements have arbitrary patterns, polarizations, and spacings. This feature is especially advantageous when an antenna array operates on an irregularly shaped surface such as an aircraft or ship.
Adaptive signal processing systems are required to filter out undesirable interference and noise. Due to the lack of a priori knowledge of an external environment, adaptive signal processing systems require a certain amount of statistically independent weight training data samples (called secondary sample data) to effectively estimate the input noise and interference statistics.
“Ideal” weight training data has a Gaussian probability distribution for both its real and imaginary baseband components. However, real-world weight training data may be contaminated by undesirable impulse noise outliers, resulting in a non-Gaussian distribution of real and imaginary components.
The number of weight training data samples required for SINR performance of the adaptive processor to be within 3 dB of the optimum on average is called the convergence measure of effectiveness (MOE) of the processor. A signal is stationary if its statistical probability distribution is independent of time. For the pure statistically stationary Gaussian noise case, the convergence MOE of the conventional Sample Matrix Inversion (SMI) adaptive linear technique can be attained using approximately 2N samples for adaptive weight estimation, regardless of the input noise covariance matrix, where N is the number of degrees of freedom in the processor (i.e., the number of antenna elements or subarrays) for a spatially adaptive array processor, or N is the number of space-time channels in a space-time adaptive processing (STAP) processor). Referred to as the SMI convergence MOE, convergence within 3 dB of the optimum using approximately 2N samples for adaptive weight estimation has become a benchmark used to assess convergence rates of full rank adaptive processors. General information regarding SMI convergence MOE may be found in Reed, I. S., Mallet, J. D., Brennan, L. E., “Rapid Convergence Rate in Adaptive Arrays”. IEEE Trans. Aerospace and Electronic Systems, Vol. AES-10, No. 6, November, 1974, pp. 853–863, the disclosure of which is incorporated herein by reference.
Conventional sample matrix inversion (SMI) adaptive signal processing systems are capable of meeting this benchmark for the pure statistically stationary Gaussian noise case. If, however, the weight training data contains non-Gaussian noise outliers, the convergence MOE of the system increases to require an unworkably large number of weight training data samples. The performance degradation of the SMI algorithm in the presence of non-Gaussian distributions (outliers) can be attributed to the highly sensitive nature of input noise covariance matrix estimates to even small amounts of impulsive non-Gaussian noise that may be corrupting the dominant Gaussian noise distribution. General information regarding the sensitivity of the SMI algorithm may be found in Antonik, P. Schuman, H. Melvin, W., Wicks, M., “Implementation of Knowledge-Based Control for Space-Time Adaptive Processing”, IEEE Radar 97 Conference, 14–16 Oct., 1997, p. 478–482, the disclosure of which is incorporated herein by reference.
Thus, for contaminated weight training data, convergence rate may slow significantly with conventional systems. Fast convergence rates are important for several practical reasons including limited amounts of weight training data due to non-stationary interference and computational complexity involved in generating adaptive weights. In other words, the time which elapses while a conventional system is acquiring weight training data and generating adaptive weights may exceed the stationary component of a given non-stationary noise environment, and an adaptive weight thus generated has become obsolete prior to completion of its computation.
Most real world data does not have a purely Gaussian probability distribution due to contamination by non-Gaussian outliers and/or desired signal components. Conventional signal processors assume that the weight training data has a Gaussian distribution, and therefore they do not perform as well as theory would predict when operating with real world data. If weight training data contains desired signals that appear to be outliers, the performance is similarly degraded.
Optimal, reduced rank, adaptive processors are derived primarily to combat the problem of non-stationary data conditions (i.e. low sample support) often encountered in general applications. However, they still have convergence MOE's that are degraded by outliers. For radar applications, these provide better SINR output than full rank methods, typically through the use of localized training data to improve statistical similarity with the range cell under test (CUT). An exemplary system is described in U.S. Pat. No. 7,120,657, “System and Method For Adaptive Filtering”, Goldstein et al., issued Oct. 10, 2006, incorporated herein by reference.
Also, full rank, robust, adaptive processor research has resulted in novel open loop processors capable of accommodating an amount of non-Gaussian/outlier contaminated and nonstationary data, while still producing an SMI-like convergence MOE. An exemplary system is described in U.S. Pat. No. 6,904,444. “Pseudo-Median Cascaded Canceller”, Picciolo et al., issued Jun. 7, 2005, incorporated herein by reference.
Reduced rank processors have a convergence MOE typically on the order of 2r, where r is the effective rank of the interference covariance matrix. Effective rank refers to that value of r which is associated with the dominant eigenvalues of the interference and noise covariance matrix. General information regarding “effective rank” and general trends in the convergence MOE of reduced rank processors may be found in “Principal Components, Covariance Matrix Tapers, and the Subspace Leakage Problem”, J. R. Guerci and J. S. Bergin, IEEE Transactions on Aerospace and Electronic Systems, Vol. 38, No. 1, January 2002.
Fundamental to most adaptive matched filter (AMF) methods is the accurate estimation of the unknown input covariance matrix. The true covariance matrix provides the optimal linear weighting of MN input elements such that the output signal-to-interference ratio is maximized, where N is the number of antenna elements and M is the number of pulses. Due to the lack of knowledge of an external environment, adaptive techniques require a certain amount of data to estimate the MN×MN input covariance matrix effectively. Minimizing the convergence MOE is important since the characteristics of the external interference change rapidly with time in many environments.
Typically for adaptive radar applications, the sample covariance matrix is estimated using training data from range cells close to the CUT. However, the presence of outliers in the training data can skew the covariance matrix estimate such that a true target in the primary range cell is suppressed. Therefore, it is important that all relevant outliers be excised.
A variety of conditions exists wherein outlier data can be present. For example, sidelobe-clutter discretes could be present in only a few range cells. The temporal covariance matrix of the sidelobe-clutter discretes is much different than that of other sources such as the surrounding sea clutter. This problem is closely related to the existence of land-sea clutter interfaces, which cause significant degradation in airborne radar adaptive processing. Other sources of outlier data are the desired targets themselves. For example, if one is trying to detect an individual target adaptively in the presence of a formation of targets (such as an airborne formation), the other desired target returns, located in distinct range cells about the individual desired target with essentially the same velocity vector, can be present in the training data. All of the desired targets have approximately the same desired steering vector. The presence of the desired target returns in the training data can severely degrade the adaptive match filter's performance, because the training data is used to estimate a weighting vector which is in the null space of the signal and interference sources that are in the training data. Hence, if a signal that has the desired signal's steering vector is in the training data, the adaptive weight vector may null the desired signal.
“Outlier Resistant Adaptive Matched Filters”, K. Gerlach, IEEE Trans. on AES, Vol. 38, No. 2, July 2002, incorporated herein by reference, describes a robust AMF whereby outlier data vectors in the training data are censored from the covariance matrix estimate using the Maximum Likelihood Estimation (MLE) setting. The Reiterative Censored Fast Maximum Likelihood (RCFML/GIP) technique is based on eliminating a fixed number of snapshots from the training set in reiterative fashion using a version of the Generalized Inner Product (GIP) metric, described in “Screening among Multivariate Normal Data”, P. Chen. W. L. Melvin. and M. C. Wicks, Journal of Multivariate Analysis, 69, pp. 10–29, 1999, and incorporated herein by reference. The RCFML's convergence performance was shown to be relatively unaffected by the presence of outliers where the interference scenario consisted of homogeneous Gaussian noise plus the outliers.
It would be desirable to extend these techniques to situations that include nonhomogeneous clutter and other such interference sources.